| Issue |
E3S Web Conf.
Volume 309, 2021
3rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|
|
|---|---|---|
| Article Number | 01039 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202130901039 | |
| Published online | 07 October 2021 | |
Survey Analysis of Solar Power Generation Forecasting
1 MTech Student, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
2 Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
3 Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
4 Asst.Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
5 Asst.Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
Solar power is the conversion of sunlight into electricity using solar photovoltaic cells as a source of energy. There are various applications for solar power; here is information on PV cell generation. We seek to understand the behavior of solar power plants through the data generated by the photovoltaic modules and the power generation in different weather conditions in India. The goal of this survey is to give a thorough assessment and study of machine learning, deep learning and artificial intelligence. Artificial intelligence (AI) models as well as information preprocessing techniques, parameter selection algorithms and predictive performance evaluations are used in machine learning and deep learning models for predicting renewable energies. But in case of time series data we can predict only the errors using a linear regression model, we can also calculate things like root mean square error (RMSE), mean absolute error (MSE), mean bias error (MBE) and mean absolute percentage error (MAPE). By the analysis of weather condition also we can predict the consumption of current by solar for every 15 minutes, 1day, and 1week or even for 1 month and find the accuracy.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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